Singular Value Decomposition (SVD) yields a method to decompose each mn×pq matrix M into a sum with a minimum number of terms, each of which is the Kronecker product of an m×p matrix by and n×q matrix. This decomposition is known as a Schmidt decomposition of M. We shall say that M is decomposed with respect to the decomposition shape (m, n, p, q). Assuming that M represents a digital file, dropping some terms from the decomposition of M and using the other terms to build a matrix that approximates M leads to a lossy compression of the digital file. In addition to this compression method, there is another compression method based on SVD known as compression with SVD. Every compression method based on SVD has an energy-compaction property which causes the method to be useful for compression. With SVD, singular values and vectors are to be stored to construct the output file. These values and entries are not necessarily integers even if all entries in the original matrix are integers. Thus, storing in a computer the singular values and vectors without losing too much information requires per pixel a much larger amount of memory space than the amount of memory space occupied by a pixel in the original file. Therefore, the compression ratio with SVD is not as desirable in comparison with ratios achieved by other existing compression methods, such as JPEG [see references 4-6, below]. Other compressions schemes based on algebra include algorithms based on QR and QLP decompositions, [1A].